screenpipe vs Pieces for Developers (2026)

screenpipe

by Mediar, Inc.

Pieces for Developers

by Pieces

Pros

• ✓Local-first by architecture rather than by policy: capture, storage and search all sit on your own machine, cloud model calls send only the query, and Ollama support means the whole loop can run without a single cloud call.
• ✓A first-party MCP server rather than a claim: npx -y screenpipe-mcp exposes search-content and export-video over a local REST API on port 3030, with a one-click install into Claude Desktop from the app's own settings panel and documented stdio setups for Claude Code, OpenAI Codex, Cursor and Warp.
• ✓Nothing about the price or the setup is gated: all three tiers publish a rate with no quote-only step and the currency sits in the site's own structured data, and getting running is a desktop install on macOS, Windows or Linux against a documented five-minute quickstart.

Limitations

• ⚠The trust portal shows SOC 2 Type 2, ISO 27001, GDPR and HIPAA all as COMPLIANT with no in-progress qualifier anywhere on the page, and the site's own security page does not corroborate it, so we publish only SOC 2 Type II, GDPR and CCPA. There is little independent evidence to check the vendor against either: 21,150 GitHub stars but only two Product Hunt reviews and no G2 profile.
• ⚠Section 4 of the terms has you consent, by using the service, to the vendor creating Deidentified Data and Analytics Data from your usage and using it to improve and train its models. Customer content, output and personal information are excluded without separate written agreement, but the deidentified pathway is consent-by-use rather than opt-in.
• ⚠Public source is not an open-source licence: commercial use requires a paid licence under the Screenpipe Commercial License, and GitHub reports the repository licence as Other. Running it also costs roughly 5 to 10 GB of local disk per month, and on macOS the app captures nothing until screen recording and accessibility access are granted in System Settings.

Pros

• ✓Cross-tool memory architecture with 10 dedicated plugins captures context that IDE-only tools miss: VS Code (95,000+ installs), JetBrains (34,000+), Visual Studio (15,000+), Obsidian (10,000+), JupyterLab, Sublime Text, Neovim, browser extension, CLI, and Raycast cover the full developer surface.
• ✓Local-first processing with explicit no-training commitment and SOC 2 Type II certification: Pieces processes data locally where possible, supports on-device models via Ollama and proprietary nano-models, and commits to never using customer data, addressing privacy blockers at security-conscious organizations.
• ✓MCP support connects Pieces memory to GitHub Copilot, Claude, Cursor, and Goose: the persistent memory layer complements rather than competes with AI coding agents, letting developers use their preferred coding tool with Pieces providing the context those tools lack.

Limitations

• ⚠Memory tool category requires investment before delivering value: developers must accumulate context over time before the LTM advantage materializes, with longer time-to-meaningful-benefit than tools that work immediately on first use.
• ⚠Teams pricing requires contacting sales with no self-serve option: organizations cannot evaluate team features like shared memory and BYOK model selection without a sales conversation, adding friction compared to competitors with self-serve team trials.
• ⚠Not an autonomous coding agent: Pieces is a memory and context layer that complements but does not replace AI coding tools like Cursor or Claude Code; teams expecting autonomous code generation, bug fixing, or PR creation will not find those capabilities here.